diff --git a/autogen/agentchat/contrib/vectordb/base.py b/autogen/agentchat/contrib/vectordb/base.py
index 1531a6494c..20b6376d01 100644
--- a/autogen/agentchat/contrib/vectordb/base.py
+++ b/autogen/agentchat/contrib/vectordb/base.py
@@ -1,4 +1,16 @@
-from typing import Any, List, Mapping, Optional, Protocol, Sequence, Tuple, TypedDict, Union, runtime_checkable
+from typing import (
+    Any,
+    Callable,
+    List,
+    Mapping,
+    Optional,
+    Protocol,
+    Sequence,
+    Tuple,
+    TypedDict,
+    Union,
+    runtime_checkable,
+)
 
 Metadata = Union[Mapping[str, Any], None]
 Vector = Union[Sequence[float], Sequence[int]]
@@ -49,6 +61,9 @@ class VectorDB(Protocol):
 
     active_collection: Any = None
     type: str = ""
+    embedding_function: Optional[Callable[[List[str]], List[List[float]]]] = (
+        None  # embeddings = embedding_function(sentences)
+    )
 
     def create_collection(self, collection_name: str, overwrite: bool = False, get_or_create: bool = True) -> Any:
         """
diff --git a/autogen/agentchat/contrib/vectordb/qdrant.py b/autogen/agentchat/contrib/vectordb/qdrant.py
index 479d7a4852..398734eb03 100644
--- a/autogen/agentchat/contrib/vectordb/qdrant.py
+++ b/autogen/agentchat/contrib/vectordb/qdrant.py
@@ -92,7 +92,7 @@ class QdrantVectorDB(VectorDB):
             collection_options: dict | The options for creating the collection.
             kwargs: dict | Additional keyword arguments.
         """
-        self.client: QdrantClient = client if client is not None else QdrantClient(location=":memory:")
+        self.client: QdrantClient = client or QdrantClient(location=":memory:")
         self.embedding_function = FastEmbedEmbeddingFunction() or embedding_function
         self.collection_options = collection_options
         self.content_payload_key = content_payload_key
@@ -102,6 +102,10 @@ class QdrantVectorDB(VectorDB):
     def create_collection(self, collection_name: str, overwrite: bool = False, get_or_create: bool = True) -> None:
         """
         Create a collection in the vector database.
+        Case 1. if the collection does not exist, create the collection.
+        Case 2. the collection exists, if overwrite is True, it will overwrite the collection.
+        Case 3. the collection exists and overwrite is False, if get_or_create is True, it will get the collection,
+            otherwise it raise a ValueError.
 
         Args:
             collection_name: str | The name of the collection.
@@ -122,6 +126,8 @@ class QdrantVectorDB(VectorDB):
                 vectors_config=models.VectorParams(size=embeddings_size, distance=models.Distance.COSINE),
                 **self.collection_options,
             )
+        elif not get_or_create:
+            raise ValueError(f"Collection {collection_name} already exists.")
 
     def get_collection(self, collection_name: str = None):
         """
diff --git a/notebook/agentchat_pgvector_RetrieveChat.ipynb b/notebook/agentchat_RetrieveChat_pgvector.ipynb
similarity index 100%
rename from notebook/agentchat_pgvector_RetrieveChat.ipynb
rename to notebook/agentchat_RetrieveChat_pgvector.ipynb
diff --git a/notebook/agentchat_qdrant_RetrieveChat.ipynb b/notebook/agentchat_RetrieveChat_qdrant.ipynb
similarity index 50%
rename from notebook/agentchat_qdrant_RetrieveChat.ipynb
rename to notebook/agentchat_RetrieveChat_qdrant.ipynb
index 10d6b55e11..b5bc2f681d 100644
--- a/notebook/agentchat_qdrant_RetrieveChat.ipynb
+++ b/notebook/agentchat_RetrieveChat_qdrant.ipynb
@@ -31,172 +31,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
-      "To disable this warning, you can either:\n",
-      "\t- Avoid using `tokenizers` before the fork if possible\n",
-      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
-      "Requirement already satisfied: pyautogen>=0.2.3 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from pyautogen[retrievechat]>=0.2.3) (0.2.21)\n",
-      "Requirement already satisfied: flaml[automl] in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (2.1.2)\n",
-      "Requirement already satisfied: qdrant_client[fastembed] in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (1.6.4)\n",
-      "Requirement already satisfied: openai>=1.3 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from pyautogen>=0.2.3->pyautogen[retrievechat]>=0.2.3) (1.12.0)\n",
-      "Requirement already satisfied: diskcache in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from pyautogen>=0.2.3->pyautogen[retrievechat]>=0.2.3) (5.6.3)\n",
-      "Requirement already satisfied: termcolor in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from pyautogen>=0.2.3->pyautogen[retrievechat]>=0.2.3) (2.3.0)\n",
-      "Requirement already satisfied: numpy<2,>=1.17.0 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from pyautogen>=0.2.3->pyautogen[retrievechat]>=0.2.3) (1.26.4)\n",
-      "Requirement already satisfied: python-dotenv in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from pyautogen>=0.2.3->pyautogen[retrievechat]>=0.2.3) (1.0.0)\n",
-      "Requirement already satisfied: tiktoken in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from pyautogen>=0.2.3->pyautogen[retrievechat]>=0.2.3) (0.5.1)\n",
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-      "Requirement already satisfied: docker in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from pyautogen>=0.2.3->pyautogen[retrievechat]>=0.2.3) (7.0.0)\n",
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-      "Requirement already satisfied: xgboost>=0.90 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from flaml[automl]) (2.0.1)\n",
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-      "Requirement already satisfied: pandas>=1.1.4 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from flaml[automl]) (2.2.0)\n",
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-      "Requirement already satisfied: filelock in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from huggingface-hub>=0.15.1->sentence-transformers->pyautogen[retrievechat]>=0.2.3) (3.13.1)\n",
-      "Requirement already satisfied: fsspec>=2023.5.0 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from huggingface-hub>=0.15.1->sentence-transformers->pyautogen[retrievechat]>=0.2.3) (2024.2.0)\n",
-      "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from jedi>=0.16->ipython->pyautogen[retrievechat]>=0.2.3) (0.8.3)\n",
-      "Requirement already satisfied: six>=1.9.0 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from kubernetes>=28.1.0->chromadb->pyautogen[retrievechat]>=0.2.3) (1.16.0)\n",
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-      "Requirement already satisfied: websocket-client!=0.40.0,!=0.41.*,!=0.42.*,>=0.32.0 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from kubernetes>=28.1.0->chromadb->pyautogen[retrievechat]>=0.2.3) (1.6.4)\n",
-      "Requirement already satisfied: requests-oauthlib in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from kubernetes>=28.1.0->chromadb->pyautogen[retrievechat]>=0.2.3) (1.3.1)\n",
-      "Requirement already satisfied: oauthlib>=3.2.2 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from kubernetes>=28.1.0->chromadb->pyautogen[retrievechat]>=0.2.3) (3.2.2)\n",
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-      "Requirement already satisfied: flatbuffers in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from onnxruntime<2.0,>=1.15->fastembed==0.1.1->qdrant_client[fastembed]) (23.5.26)\n",
-      "Requirement already satisfied: sympy in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from onnxruntime<2.0,>=1.15->fastembed==0.1.1->qdrant_client[fastembed]) (1.12)\n",
-      "Requirement already satisfied: deprecated>=1.2.6 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from opentelemetry-api>=1.2.0->chromadb->pyautogen[retrievechat]>=0.2.3) (1.2.14)\n",
-      "Requirement already satisfied: importlib-metadata<7.0,>=6.0 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from opentelemetry-api>=1.2.0->chromadb->pyautogen[retrievechat]>=0.2.3) (6.11.0)\n",
-      "Requirement already satisfied: backoff<3.0.0,>=1.10.0 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from opentelemetry-exporter-otlp-proto-grpc>=1.2.0->chromadb->pyautogen[retrievechat]>=0.2.3) (2.2.1)\n",
-      "Requirement already satisfied: googleapis-common-protos~=1.52 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from opentelemetry-exporter-otlp-proto-grpc>=1.2.0->chromadb->pyautogen[retrievechat]>=0.2.3) (1.61.0)\n",
-      "Requirement already satisfied: opentelemetry-exporter-otlp-proto-common==1.20.0 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from opentelemetry-exporter-otlp-proto-grpc>=1.2.0->chromadb->pyautogen[retrievechat]>=0.2.3) (1.20.0)\n",
-      "Requirement already satisfied: opentelemetry-proto==1.20.0 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from opentelemetry-exporter-otlp-proto-grpc>=1.2.0->chromadb->pyautogen[retrievechat]>=0.2.3) (1.20.0)\n",
-      "Requirement already satisfied: opentelemetry-instrumentation-asgi==0.41b0 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from opentelemetry-instrumentation-fastapi>=0.41b0->chromadb->pyautogen[retrievechat]>=0.2.3) (0.41b0)\n",
-      "Requirement already satisfied: opentelemetry-instrumentation==0.41b0 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from opentelemetry-instrumentation-fastapi>=0.41b0->chromadb->pyautogen[retrievechat]>=0.2.3) (0.41b0)\n",
-      "Requirement already satisfied: opentelemetry-semantic-conventions==0.41b0 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from opentelemetry-instrumentation-fastapi>=0.41b0->chromadb->pyautogen[retrievechat]>=0.2.3) (0.41b0)\n",
-      "Requirement already satisfied: opentelemetry-util-http==0.41b0 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from opentelemetry-instrumentation-fastapi>=0.41b0->chromadb->pyautogen[retrievechat]>=0.2.3) (0.41b0)\n",
-      "Requirement already satisfied: wrapt<2.0.0,>=1.0.0 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from opentelemetry-instrumentation==0.41b0->opentelemetry-instrumentation-fastapi>=0.41b0->chromadb->pyautogen[retrievechat]>=0.2.3) (1.16.0)\n",
-      "Requirement already satisfied: asgiref~=3.0 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from opentelemetry-instrumentation-asgi==0.41b0->opentelemetry-instrumentation-fastapi>=0.41b0->chromadb->pyautogen[retrievechat]>=0.2.3) (3.7.2)\n",
-      "Requirement already satisfied: ptyprocess>=0.5 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from pexpect>4.3->ipython->pyautogen[retrievechat]>=0.2.3) (0.7.0)\n",
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-      "Requirement already satisfied: wcwidth in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30->ipython->pyautogen[retrievechat]>=0.2.3) (0.2.9)\n",
-      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from requests<3.0,>=2.31->fastembed==0.1.1->qdrant_client[fastembed]) (3.3.2)\n",
-      "Requirement already satisfied: networkx in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from torch>=1.11.0->sentence-transformers->pyautogen[retrievechat]>=0.2.3) (3.2.1)\n",
-      "Requirement already satisfied: jinja2 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from torch>=1.11.0->sentence-transformers->pyautogen[retrievechat]>=0.2.3) (3.1.3)\n",
-      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from torch>=1.11.0->sentence-transformers->pyautogen[retrievechat]>=0.2.3) (12.1.105)\n",
-      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from torch>=1.11.0->sentence-transformers->pyautogen[retrievechat]>=0.2.3) (12.1.105)\n",
-      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from torch>=1.11.0->sentence-transformers->pyautogen[retrievechat]>=0.2.3) (12.1.105)\n",
-      "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from torch>=1.11.0->sentence-transformers->pyautogen[retrievechat]>=0.2.3) (8.9.2.26)\n",
-      "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from torch>=1.11.0->sentence-transformers->pyautogen[retrievechat]>=0.2.3) (12.1.3.1)\n",
-      "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from torch>=1.11.0->sentence-transformers->pyautogen[retrievechat]>=0.2.3) (11.0.2.54)\n",
-      "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from torch>=1.11.0->sentence-transformers->pyautogen[retrievechat]>=0.2.3) (10.3.2.106)\n",
-      "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from torch>=1.11.0->sentence-transformers->pyautogen[retrievechat]>=0.2.3) (11.4.5.107)\n",
-      "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from torch>=1.11.0->sentence-transformers->pyautogen[retrievechat]>=0.2.3) (12.1.0.106)\n",
-      "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from torch>=1.11.0->sentence-transformers->pyautogen[retrievechat]>=0.2.3) (2.19.3)\n",
-      "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from torch>=1.11.0->sentence-transformers->pyautogen[retrievechat]>=0.2.3) (12.1.105)\n",
-      "Requirement already satisfied: triton==2.2.0 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from torch>=1.11.0->sentence-transformers->pyautogen[retrievechat]>=0.2.3) (2.2.0)\n",
-      "Requirement already satisfied: nvidia-nvjitlink-cu12 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.11.0->sentence-transformers->pyautogen[retrievechat]>=0.2.3) (12.3.52)\n",
-      "Requirement already satisfied: safetensors>=0.3.1 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from transformers<5.0.0,>=4.32.0->sentence-transformers->pyautogen[retrievechat]>=0.2.3) (0.3.2)\n",
-      "Requirement already satisfied: click<9.0.0,>=7.1.1 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from typer>=0.9.0->chromadb->pyautogen[retrievechat]>=0.2.3) (8.1.7)\n",
-      "Requirement already satisfied: h11>=0.8 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from uvicorn>=0.18.3->uvicorn[standard]>=0.18.3->chromadb->pyautogen[retrievechat]>=0.2.3) (0.14.0)\n",
-      "Requirement already satisfied: httptools>=0.5.0 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from uvicorn[standard]>=0.18.3->chromadb->pyautogen[retrievechat]>=0.2.3) (0.6.1)\n",
-      "Requirement already satisfied: uvloop!=0.15.0,!=0.15.1,>=0.14.0 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from uvicorn[standard]>=0.18.3->chromadb->pyautogen[retrievechat]>=0.2.3) (0.19.0)\n",
-      "Requirement already satisfied: watchfiles>=0.13 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from uvicorn[standard]>=0.18.3->chromadb->pyautogen[retrievechat]>=0.2.3) (0.21.0)\n",
-      "Requirement already satisfied: websockets>=10.4 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from uvicorn[standard]>=0.18.3->chromadb->pyautogen[retrievechat]>=0.2.3) (11.0.3)\n",
-      "Requirement already satisfied: executing>=1.2.0 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from stack-data->ipython->pyautogen[retrievechat]>=0.2.3) (2.0.1)\n",
-      "Requirement already satisfied: asttokens>=2.1.0 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from stack-data->ipython->pyautogen[retrievechat]>=0.2.3) (2.4.1)\n",
-      "Requirement already satisfied: pure-eval in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from stack-data->ipython->pyautogen[retrievechat]>=0.2.3) (0.2.2)\n",
-      "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from google-auth>=1.0.1->kubernetes>=28.1.0->chromadb->pyautogen[retrievechat]>=0.2.3) (5.3.2)\n",
-      "Requirement already satisfied: pyasn1-modules>=0.2.1 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from google-auth>=1.0.1->kubernetes>=28.1.0->chromadb->pyautogen[retrievechat]>=0.2.3) (0.3.0)\n",
-      "Requirement already satisfied: rsa<5,>=3.1.4 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from google-auth>=1.0.1->kubernetes>=28.1.0->chromadb->pyautogen[retrievechat]>=0.2.3) (4.9)\n",
-      "Requirement already satisfied: zipp>=0.5 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from importlib-metadata<7.0,>=6.0->opentelemetry-api>=1.2.0->chromadb->pyautogen[retrievechat]>=0.2.3) (3.17.0)\n",
-      "Requirement already satisfied: humanfriendly>=9.1 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from coloredlogs->onnxruntime<2.0,>=1.15->fastembed==0.1.1->qdrant_client[fastembed]) (10.0)\n",
-      "Requirement already satisfied: MarkupSafe>=2.0 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from jinja2->torch>=1.11.0->sentence-transformers->pyautogen[retrievechat]>=0.2.3) (2.1.5)\n",
-      "Requirement already satisfied: mpmath>=0.19 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from sympy->onnxruntime<2.0,>=1.15->fastembed==0.1.1->qdrant_client[fastembed]) (1.3.0)\n",
-      "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /home/lijiang1/anaconda3/envs/autogen/lib/python3.10/site-packages (from pyasn1-modules>=0.2.1->google-auth>=1.0.1->kubernetes>=28.1.0->chromadb->pyautogen[retrievechat]>=0.2.3) (0.5.0)\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
    ],
    "source": [
-    "%pip install \"pyautogen[retrievechat-qdrant]\" \"flaml[automl]\""
+    "%pip install \"pyautogen[retrievechat-qdrant]\" \"flaml[automl]\" -q"
    ]
   },
   {
@@ -211,23 +58,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "models to use:  ['gpt4-1106-preview', 'gpt-35-turbo', 'gpt-35-turbo-0613']\n"
+      "models to use:  ['gpt4-1106-preview', 'gpt-4o', 'gpt-35-turbo', 'gpt-35-turbo-0613']\n"
      ]
     }
    ],
    "source": [
     "from qdrant_client import QdrantClient\n",
+    "from sentence_transformers import SentenceTransformer\n",
     "\n",
     "import autogen\n",
-    "from autogen.agentchat.contrib.qdrant_retrieve_user_proxy_agent import QdrantRetrieveUserProxyAgent\n",
     "from autogen.agentchat.contrib.retrieve_assistant_agent import RetrieveAssistantAgent\n",
+    "from autogen.agentchat.contrib.retrieve_user_proxy_agent import RetrieveUserProxyAgent\n",
     "\n",
     "# Accepted file formats for that can be stored in\n",
     "# a vector database instance\n",
@@ -253,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -261,7 +109,7 @@
      "output_type": "stream",
      "text": [
       "Accepted file formats for `docs_path`:\n",
-      "['yml', 'ppt', 'org', 'doc', 'epub', 'rst', 'log', 'docx', 'htm', 'html', 'tsv', 'csv', 'json', 'yaml', 'xlsx', 'pptx', 'rtf', 'msg', 'odt', 'pdf', 'jsonl', 'md', 'xml', 'txt']\n"
+      "['rtf', 'jsonl', 'xml', 'json', 'md', 'rst', 'docx', 'msg', 'pdf', 'log', 'xlsx', 'org', 'txt', 'csv', 'pptx', 'tsv', 'yml', 'epub', 'yaml', 'ppt', 'htm', 'doc', 'odt', 'html']\n"
      ]
     }
    ],
@@ -284,9 +132,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67171b10626248ba8b5bff0f5a4d6895",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Fetching 5 files:   0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# 1. create an RetrieveAssistantAgent instance named \"assistant\"\n",
     "assistant = RetrieveAssistantAgent(\n",
@@ -299,7 +162,11 @@
     "    },\n",
     ")\n",
     "\n",
-    "# 2. create the QdrantRetrieveUserProxyAgent instance named \"ragproxyagent\"\n",
+    "# Optionally create embedding function object\n",
+    "sentence_transformer_ef = SentenceTransformer(\"all-distilroberta-v1\").encode\n",
+    "client = QdrantClient(\":memory:\")\n",
+    "\n",
+    "# 2. create the RetrieveUserProxyAgent instance named \"ragproxyagent\"\n",
     "# By default, the human_input_mode is \"ALWAYS\", which means the agent will ask for human input at every step. We set it to \"NEVER\" here.\n",
     "# `docs_path` is the path to the docs directory. It can also be the path to a single file, or the url to a single file. By default,\n",
     "# it is set to None, which works only if the collection is already created.\n",
@@ -312,7 +179,7 @@
     "# `chunk_token_size` is the chunk token size for the retrieve chat. By default, it is set to `max_tokens * 0.6`, here we set it to 2000.\n",
     "# We use an in-memory QdrantClient instance here. Not recommended for production.\n",
     "# Get the installation instructions here: https://qdrant.tech/documentation/guides/installation/\n",
-    "ragproxyagent = QdrantRetrieveUserProxyAgent(\n",
+    "ragproxyagent = RetrieveUserProxyAgent(\n",
     "    name=\"ragproxyagent\",\n",
     "    human_input_mode=\"NEVER\",\n",
     "    max_consecutive_auto_reply=10,\n",
@@ -324,8 +191,11 @@
     "        ],  # change this to your own path, such as https://raw.githubusercontent.com/microsoft/autogen/main/README.md\n",
     "        \"chunk_token_size\": 2000,\n",
     "        \"model\": config_list[0][\"model\"],\n",
-    "        \"client\": QdrantClient(\":memory:\"),\n",
-    "        \"embedding_model\": \"BAAI/bge-small-en-v1.5\",\n",
+    "        \"db_config\": {\"client\": client},\n",
+    "        \"vector_db\": \"qdrant\",  # qdrant database\n",
+    "        \"get_or_create\": True,  # set to False if you don't want to reuse an existing collection\n",
+    "        \"overwrite\": True,  # set to True if you want to overwrite an existing collection\n",
+    "        \"embedding_function\": sentence_transformer_ef,  # If left out fastembed \"BAAI/bge-small-en-v1.5\" will be used\n",
     "    },\n",
     "    code_execution_config=False,\n",
     ")"
@@ -348,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -362,7 +232,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-04-07 18:30:12,489 - autogen.agentchat.contrib.qdrant_retrieve_user_proxy_agent - INFO - Found 3 chunks.\u001b[0m\n",
+      "2024-07-15 23:19:34,988 - autogen.agentchat.contrib.retrieve_user_proxy_agent - INFO - Found 3 chunks.\u001b[0m\n",
       "Model gpt4-1106-preview not found. Using cl100k_base encoding.\n"
      ]
     },
@@ -370,7 +240,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[32mAdding content of doc 0 to context.\u001b[0m\n"
+      "VectorDB returns doc_ids:  [['987f060a-4399-b91a-0e51-51b6165ea5bb', '0ecd7192-3761-7d6f-9151-5ff504ca740b', 'ddbaaafc-abdd-30b4-eecd-ec2c32818952']]\n",
+      "\u001b[32mAdding content of doc 987f060a-4399-b91a-0e51-51b6165ea5bb to context.\u001b[0m\n"
      ]
     },
     {
@@ -603,7 +474,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[32mAdding content of doc 2 to context.\u001b[0m\n"
+      "\u001b[32mAdding content of doc 0ecd7192-3761-7d6f-9151-5ff504ca740b to context.\u001b[0m\n"
      ]
     },
     {
@@ -617,7 +488,146 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[32mAdding content of doc 1 to context.\u001b[0m\n",
+      "\u001b[32mAdding content of doc ddbaaafc-abdd-30b4-eecd-ec2c32818952 to context.\u001b[0m\n",
+      "\u001b[33mragproxyagent\u001b[0m (to assistant):\n",
+      "\n",
+      "You're a retrieve augmented coding assistant. You answer user's questions based on your own knowledge and the\n",
+      "context provided by the user.\n",
+      "If you can't answer the question with or without the current context, you should reply exactly `UPDATE CONTEXT`.\n",
+      "For code generation, you must obey the following rules:\n",
+      "Rule 1. You MUST NOT install any packages because all the packages needed are already installed.\n",
+      "Rule 2. You must follow the formats below to write your code:\n",
+      "```language\n",
+      "# your code\n",
+      "```\n",
+      "\n",
+      "User's question is: Is there a function called tune_automl?\n",
+      "\n",
+      "Context is: # Research\n",
+      "\n",
+      "For technical details, please check our research publications.\n",
+      "\n",
+      "- [FLAML: A Fast and Lightweight AutoML Library](https://www.microsoft.com/en-us/research/publication/flaml-a-fast-and-lightweight-automl-library/). Chi Wang, Qingyun Wu, Markus Weimer, Erkang Zhu. MLSys 2021.\n",
+      "\n",
+      "```bibtex\n",
+      "@inproceedings{wang2021flaml,\n",
+      "    title={FLAML: A Fast and Lightweight AutoML Library},\n",
+      "    author={Chi Wang and Qingyun Wu and Markus Weimer and Erkang Zhu},\n",
+      "    year={2021},\n",
+      "    booktitle={MLSys},\n",
+      "}\n",
+      "```\n",
+      "\n",
+      "- [Frugal Optimization for Cost-related Hyperparameters](https://arxiv.org/abs/2005.01571). Qingyun Wu, Chi Wang, Silu Huang. AAAI 2021.\n",
+      "\n",
+      "```bibtex\n",
+      "@inproceedings{wu2021cfo,\n",
+      "    title={Frugal Optimization for Cost-related Hyperparameters},\n",
+      "    author={Qingyun Wu and Chi Wang and Silu Huang},\n",
+      "    year={2021},\n",
+      "    booktitle={AAAI},\n",
+      "}\n",
+      "```\n",
+      "\n",
+      "- [Economical Hyperparameter Optimization With Blended Search Strategy](https://www.microsoft.com/en-us/research/publication/economical-hyperparameter-optimization-with-blended-search-strategy/). Chi Wang, Qingyun Wu, Silu Huang, Amin Saied. ICLR 2021.\n",
+      "\n",
+      "```bibtex\n",
+      "@inproceedings{wang2021blendsearch,\n",
+      "    title={Economical Hyperparameter Optimization With Blended Search Strategy},\n",
+      "    author={Chi Wang and Qingyun Wu and Silu Huang and Amin Saied},\n",
+      "    year={2021},\n",
+      "    booktitle={ICLR},\n",
+      "}\n",
+      "```\n",
+      "\n",
+      "- [An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models](https://aclanthology.org/2021.acl-long.178.pdf). Susan Xueqing Liu, Chi Wang. ACL 2021.\n",
+      "\n",
+      "```bibtex\n",
+      "@inproceedings{liuwang2021hpolm,\n",
+      "    title={An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models},\n",
+      "    author={Susan Xueqing Liu and Chi Wang},\n",
+      "    year={2021},\n",
+      "    booktitle={ACL},\n",
+      "}\n",
+      "```\n",
+      "\n",
+      "- [ChaCha for Online AutoML](https://www.microsoft.com/en-us/research/publication/chacha-for-online-automl/). Qingyun Wu, Chi Wang, John Langford, Paul Mineiro and Marco Rossi. ICML 2021.\n",
+      "\n",
+      "```bibtex\n",
+      "@inproceedings{wu2021chacha,\n",
+      "    title={ChaCha for Online AutoML},\n",
+      "    author={Qingyun Wu and Chi Wang and John Langford and Paul Mineiro and Marco Rossi},\n",
+      "    year={2021},\n",
+      "    booktitle={ICML},\n",
+      "}\n",
+      "```\n",
+      "\n",
+      "- [Fair AutoML](https://arxiv.org/abs/2111.06495). Qingyun Wu, Chi Wang. ArXiv preprint arXiv:2111.06495 (2021).\n",
+      "\n",
+      "```bibtex\n",
+      "@inproceedings{wuwang2021fairautoml,\n",
+      "    title={Fair AutoML},\n",
+      "    author={Qingyun Wu and Chi Wang},\n",
+      "    year={2021},\n",
+      "    booktitle={ArXiv preprint arXiv:2111.06495},\n",
+      "}\n",
+      "```\n",
+      "\n",
+      "- [Mining Robust Default Configurations for Resource-constrained AutoML](https://arxiv.org/abs/2202.09927). Moe Kayali, Chi Wang. ArXiv preprint arXiv:2202.09927 (2022).\n",
+      "\n",
+      "```bibtex\n",
+      "@inproceedings{kayaliwang2022default,\n",
+      "    title={Mining Robust Default Configurations for Resource-constrained AutoML},\n",
+      "    author={Moe Kayali and Chi Wang},\n",
+      "    year={2022},\n",
+      "    booktitle={ArXiv preprint arXiv:2202.09927},\n",
+      "}\n",
+      "```\n",
+      "\n",
+      "- [Targeted Hyperparameter Optimization with Lexicographic Preferences Over Multiple Objectives](https://openreview.net/forum?id=0Ij9_q567Ma). Shaokun Zhang, Feiran Jia, Chi Wang, Qingyun Wu. ICLR 2023 (notable-top-5%).\n",
+      "\n",
+      "```bibtex\n",
+      "@inproceedings{zhang2023targeted,\n",
+      "    title={Targeted Hyperparameter Optimization with Lexicographic Preferences Over Multiple Objectives},\n",
+      "    author={Shaokun Zhang and Feiran Jia and Chi Wang and Qingyun Wu},\n",
+      "    booktitle={International Conference on Learning Representations},\n",
+      "    year={2023},\n",
+      "    url={https://openreview.net/forum?id=0Ij9_q567Ma},\n",
+      "}\n",
+      "```\n",
+      "\n",
+      "- [Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference](https://arxiv.org/abs/2303.04673). Chi Wang, Susan Xueqing Liu, Ahmed H. Awadallah. ArXiv preprint arXiv:2303.04673 (2023).\n",
+      "\n",
+      "```bibtex\n",
+      "@inproceedings{wang2023EcoOptiGen,\n",
+      "    title={Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference},\n",
+      "    author={Chi Wang and Susan Xueqing Liu and Ahmed H. Awadallah},\n",
+      "    year={2023},\n",
+      "    booktitle={ArXiv preprint arXiv:2303.04673},\n",
+      "}\n",
+      "```\n",
+      "\n",
+      "- [An Empirical Study on Challenging Math Problem Solving with GPT-4](https://arxiv.org/abs/2306.01337). Yiran Wu, Feiran Jia, Shaokun Zhang, Hangyu Li, Erkang Zhu, Yue Wang, Yin Tat Lee, Richard Peng, Qingyun Wu, Chi Wang. ArXiv preprint arXiv:2306.01337 (2023).\n",
+      "\n",
+      "```bibtex\n",
+      "@inproceedings{wu2023empirical,\n",
+      "    title={An Empirical Study on Challenging Math Problem Solving with GPT-4},\n",
+      "    author={Yiran Wu and Feiran Jia and Shaokun Zhang and Hangyu Li and Erkang Zhu and Yue Wang and Yin Tat Lee and Richard Peng and Qingyun Wu and Chi Wang},\n",
+      "    year={2023},\n",
+      "    booktitle={ArXiv preprint arXiv:2306.01337},\n",
+      "}\n",
+      "```\n",
+      "If you are new to GitHub [here](https://help.github.com/categories/collaborating-with-issues-and-pull-requests/) is a detailed help source on getting involved with development on GitHub.\n",
+      "\n",
+      "When you submit a pull request, a CLA bot will automatically determine whether you need to provide\n",
+      "a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions\n",
+      "provided by the bot. You will only need to do this once across all repos using our CLA.\n",
+      "\n",
+      "This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).\n",
+      "For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or\n",
+      "contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.\n",
+      "\n",
+      "\n",
       "\u001b[33mragproxyagent\u001b[0m (to assistant):\n",
       "\n",
       "You're a retrieve augmented coding assistant. You answer user's questions based on your own knowledge and the\n",
@@ -765,6 +775,10 @@
       "\n",
       "--------------------------------------------------------------------------------\n",
       "\u001b[32mUpdating context and resetting conversation.\u001b[0m\n",
+      "VectorDB returns doc_ids:  [['987f060a-4399-b91a-0e51-51b6165ea5bb', '0ecd7192-3761-7d6f-9151-5ff504ca740b', 'ddbaaafc-abdd-30b4-eecd-ec2c32818952']]\n",
+      "VectorDB returns doc_ids:  [['987f060a-4399-b91a-0e51-51b6165ea5bb', '0ecd7192-3761-7d6f-9151-5ff504ca740b', 'ddbaaafc-abdd-30b4-eecd-ec2c32818952']]\n",
+      "VectorDB returns doc_ids:  [['987f060a-4399-b91a-0e51-51b6165ea5bb', '0ecd7192-3761-7d6f-9151-5ff504ca740b', 'ddbaaafc-abdd-30b4-eecd-ec2c32818952']]\n",
+      "VectorDB returns doc_ids:  [['987f060a-4399-b91a-0e51-51b6165ea5bb', '0ecd7192-3761-7d6f-9151-5ff504ca740b', 'ddbaaafc-abdd-30b4-eecd-ec2c32818952']]\n",
       "\u001b[32mNo more context, will terminate.\u001b[0m\n",
       "\u001b[33mragproxyagent\u001b[0m (to assistant):\n",
       "\n",
@@ -772,16 +786,6 @@
       "\n",
       "--------------------------------------------------------------------------------\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "ChatResult(chat_id=None, chat_history=[{'content': 'TERMINATE', 'role': 'assistant'}], summary='', cost=({'total_cost': 0.19977, 'gpt-4': {'cost': 0.19977, 'prompt_tokens': 6153, 'completion_tokens': 253, 'total_tokens': 6406}}, {'total_cost': 0.19977, 'gpt-4': {'cost': 0.19977, 'prompt_tokens': 6153, 'completion_tokens': 253, 'total_tokens': 6406}}), human_input=[])"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -789,7 +793,7 @@
     "assistant.reset()\n",
     "\n",
     "qa_problem = \"Is there a function called tune_automl?\"\n",
-    "ragproxyagent.initiate_chat(assistant, message=ragproxyagent.message_generator, problem=qa_problem)"
+    "chat_results = ragproxyagent.initiate_chat(assistant, message=ragproxyagent.message_generator, problem=qa_problem)"
    ]
   },
   {
@@ -809,7 +813,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -823,7 +827,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[32mAdding content of doc 2 to context.\u001b[0m\n"
+      "VectorDB returns doc_ids:  [['0ecd7192-3761-7d6f-9151-5ff504ca740b', '987f060a-4399-b91a-0e51-51b6165ea5bb', 'ddbaaafc-abdd-30b4-eecd-ec2c32818952']]\n",
+      "\u001b[32mAdding content of doc 0ecd7192-3761-7d6f-9151-5ff504ca740b to context.\u001b[0m\n"
      ]
     },
     {
@@ -968,29 +973,13 @@
       "\n",
       "\n",
       "\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "--------------------------------------------------------------------------------\n",
       "\u001b[33massistant\u001b[0m (to ragproxyagent):\n",
       "\n",
       "The authors of FLAML are Chi Wang, Qingyun Wu, Markus Weimer, and Erkang Zhu.\n",
       "\n",
       "--------------------------------------------------------------------------------\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "ChatResult(chat_id=None, chat_history=[{'content': \"You're a retrieve augmented coding assistant. You answer user's questions based on your own knowledge and the\\ncontext provided by the user.\\nIf you can't answer the question with or without the current context, you should reply exactly `UPDATE CONTEXT`.\\nFor code generation, you must obey the following rules:\\nRule 1. You MUST NOT install any packages because all the packages needed are already installed.\\nRule 2. You must follow the formats below to write your code:\\n```language\\n# your code\\n```\\n\\nUser's question is: Who is the author of FLAML?\\n\\nContext is: # Research\\n\\nFor technical details, please check our research publications.\\n\\n- [FLAML: A Fast and Lightweight AutoML Library](https://www.microsoft.com/en-us/research/publication/flaml-a-fast-and-lightweight-automl-library/). Chi Wang, Qingyun Wu, Markus Weimer, Erkang Zhu. MLSys 2021.\\n\\n```bibtex\\n@inproceedings{wang2021flaml,\\n    title={FLAML: A Fast and Lightweight AutoML Library},\\n    author={Chi Wang and Qingyun Wu and Markus Weimer and Erkang Zhu},\\n    year={2021},\\n    booktitle={MLSys},\\n}\\n```\\n\\n- [Frugal Optimization for Cost-related Hyperparameters](https://arxiv.org/abs/2005.01571). Qingyun Wu, Chi Wang, Silu Huang. AAAI 2021.\\n\\n```bibtex\\n@inproceedings{wu2021cfo,\\n    title={Frugal Optimization for Cost-related Hyperparameters},\\n    author={Qingyun Wu and Chi Wang and Silu Huang},\\n    year={2021},\\n    booktitle={AAAI},\\n}\\n```\\n\\n- [Economical Hyperparameter Optimization With Blended Search Strategy](https://www.microsoft.com/en-us/research/publication/economical-hyperparameter-optimization-with-blended-search-strategy/). Chi Wang, Qingyun Wu, Silu Huang, Amin Saied. ICLR 2021.\\n\\n```bibtex\\n@inproceedings{wang2021blendsearch,\\n    title={Economical Hyperparameter Optimization With Blended Search Strategy},\\n    author={Chi Wang and Qingyun Wu and Silu Huang and Amin Saied},\\n    year={2021},\\n    booktitle={ICLR},\\n}\\n```\\n\\n- [An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models](https://aclanthology.org/2021.acl-long.178.pdf). Susan Xueqing Liu, Chi Wang. ACL 2021.\\n\\n```bibtex\\n@inproceedings{liuwang2021hpolm,\\n    title={An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models},\\n    author={Susan Xueqing Liu and Chi Wang},\\n    year={2021},\\n    booktitle={ACL},\\n}\\n```\\n\\n- [ChaCha for Online AutoML](https://www.microsoft.com/en-us/research/publication/chacha-for-online-automl/). Qingyun Wu, Chi Wang, John Langford, Paul Mineiro and Marco Rossi. ICML 2021.\\n\\n```bibtex\\n@inproceedings{wu2021chacha,\\n    title={ChaCha for Online AutoML},\\n    author={Qingyun Wu and Chi Wang and John Langford and Paul Mineiro and Marco Rossi},\\n    year={2021},\\n    booktitle={ICML},\\n}\\n```\\n\\n- [Fair AutoML](https://arxiv.org/abs/2111.06495). Qingyun Wu, Chi Wang. ArXiv preprint arXiv:2111.06495 (2021).\\n\\n```bibtex\\n@inproceedings{wuwang2021fairautoml,\\n    title={Fair AutoML},\\n    author={Qingyun Wu and Chi Wang},\\n    year={2021},\\n    booktitle={ArXiv preprint arXiv:2111.06495},\\n}\\n```\\n\\n- [Mining Robust Default Configurations for Resource-constrained AutoML](https://arxiv.org/abs/2202.09927). Moe Kayali, Chi Wang. ArXiv preprint arXiv:2202.09927 (2022).\\n\\n```bibtex\\n@inproceedings{kayaliwang2022default,\\n    title={Mining Robust Default Configurations for Resource-constrained AutoML},\\n    author={Moe Kayali and Chi Wang},\\n    year={2022},\\n    booktitle={ArXiv preprint arXiv:2202.09927},\\n}\\n```\\n\\n- [Targeted Hyperparameter Optimization with Lexicographic Preferences Over Multiple Objectives](https://openreview.net/forum?id=0Ij9_q567Ma). Shaokun Zhang, Feiran Jia, Chi Wang, Qingyun Wu. ICLR 2023 (notable-top-5%).\\n\\n```bibtex\\n@inproceedings{zhang2023targeted,\\n    title={Targeted Hyperparameter Optimization with Lexicographic Preferences Over Multiple Objectives},\\n    author={Shaokun Zhang and Feiran Jia and Chi Wang and Qingyun Wu},\\n    booktitle={International Conference on Learning Representations},\\n    year={2023},\\n    url={https://openreview.net/forum?id=0Ij9_q567Ma},\\n}\\n```\\n\\n- [Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference](https://arxiv.org/abs/2303.04673). Chi Wang, Susan Xueqing Liu, Ahmed H. Awadallah. ArXiv preprint arXiv:2303.04673 (2023).\\n\\n```bibtex\\n@inproceedings{wang2023EcoOptiGen,\\n    title={Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference},\\n    author={Chi Wang and Susan Xueqing Liu and Ahmed H. Awadallah},\\n    year={2023},\\n    booktitle={ArXiv preprint arXiv:2303.04673},\\n}\\n```\\n\\n- [An Empirical Study on Challenging Math Problem Solving with GPT-4](https://arxiv.org/abs/2306.01337). Yiran Wu, Feiran Jia, Shaokun Zhang, Hangyu Li, Erkang Zhu, Yue Wang, Yin Tat Lee, Richard Peng, Qingyun Wu, Chi Wang. ArXiv preprint arXiv:2306.01337 (2023).\\n\\n```bibtex\\n@inproceedings{wu2023empirical,\\n    title={An Empirical Study on Challenging Math Problem Solving with GPT-4},\\n    author={Yiran Wu and Feiran Jia and Shaokun Zhang and Hangyu Li and Erkang Zhu and Yue Wang and Yin Tat Lee and Richard Peng and Qingyun Wu and Chi Wang},\\n    year={2023},\\n    booktitle={ArXiv preprint arXiv:2306.01337},\\n}\\n```\\n\\n\", 'role': 'assistant'}, {'content': 'The authors of FLAML are Chi Wang, Qingyun Wu, Markus Weimer, and Erkang Zhu.', 'role': 'user'}], summary='The authors of FLAML are Chi Wang, Qingyun Wu, Markus Weimer, and Erkang Zhu.', cost=({'total_cost': 0.04596, 'gpt-4': {'cost': 0.04596, 'prompt_tokens': 1486, 'completion_tokens': 23, 'total_tokens': 1509}}, {'total_cost': 0.04596, 'gpt-4': {'cost': 0.04596, 'prompt_tokens': 1486, 'completion_tokens': 23, 'total_tokens': 1509}}), human_input=[])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -998,7 +987,7 @@
     "assistant.reset()\n",
     "\n",
     "qa_problem = \"Who is the author of FLAML?\"\n",
-    "ragproxyagent.initiate_chat(assistant, message=ragproxyagent.message_generator, problem=qa_problem)"
+    "chat_results = ragproxyagent.initiate_chat(assistant, message=ragproxyagent.message_generator, problem=qa_problem)"
    ]
   }
  ],
diff --git a/website/blog/2023-10-18-RetrieveChat/index.mdx b/website/blog/2023-10-18-RetrieveChat/index.mdx
index 1685b22f5d..12ee030513 100644
--- a/website/blog/2023-10-18-RetrieveChat/index.mdx
+++ b/website/blog/2023-10-18-RetrieveChat/index.mdx
@@ -483,4 +483,4 @@ The online app and the source code are hosted in [HuggingFace](https://huggingfa
 You can check out more example notebooks for RAG use cases:
 - [Automated Code Generation and Question Answering with Retrieval Augmented Agents](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_RetrieveChat.ipynb)
 - [Group Chat with Retrieval Augmented Generation (with 5 group member agents and 1 manager agent)](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat_RAG.ipynb)
-- [Automated Code Generation and Question Answering with Qdrant based Retrieval Augmented Agents](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_qdrant_RetrieveChat.ipynb)
+- [Automated Code Generation and Question Answering with Qdrant based Retrieval Augmented Agents](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_RetrieveChat_qdrant.ipynb)
diff --git a/website/docs/Examples.md b/website/docs/Examples.md
index 3b71dd682e..5efd71748f 100644
--- a/website/docs/Examples.md
+++ b/website/docs/Examples.md
@@ -11,7 +11,7 @@ Links to notebook examples:
 
 - Automated Task Solving with Code Generation, Execution & Debugging - [View Notebook](/docs/notebooks/agentchat_auto_feedback_from_code_execution)
 - Automated Code Generation and Question Answering with Retrieval Augmented Agents - [View Notebook](/docs/notebooks/agentchat_RetrieveChat)
-- Automated Code Generation and Question Answering with [Qdrant](https://qdrant.tech/) based Retrieval Augmented Agents - [View Notebook](/docs/notebooks/agentchat_qdrant_RetrieveChat)
+- Automated Code Generation and Question Answering with [Qdrant](https://qdrant.tech/) based Retrieval Augmented Agents - [View Notebook](/docs/notebooks/agentchat_RetrieveChat_qdrant)
 
 ### Multi-Agent Collaboration (>3 Agents)
 
diff --git a/website/docs/ecosystem/pgvector.md b/website/docs/ecosystem/pgvector.md
index c455595eda..99afa676e7 100644
--- a/website/docs/ecosystem/pgvector.md
+++ b/website/docs/ecosystem/pgvector.md
@@ -2,4 +2,4 @@
 
 [PGVector](https://github.com/pgvector/pgvector) is an open-source vector similarity search for Postgres.
 
-- [PGVector + AutoGen Code Examples](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_pgvector_RetrieveChat.ipynb)
+- [PGVector + AutoGen Code Examples](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_RetrieveChat_pgvector.ipynb)
diff --git a/website/docs/installation/Optional-Dependencies.md b/website/docs/installation/Optional-Dependencies.md
index 13991023f8..2d0067c995 100644
--- a/website/docs/installation/Optional-Dependencies.md
+++ b/website/docs/installation/Optional-Dependencies.md
@@ -75,7 +75,7 @@ Example notebooks:
 
 [Group Chat with Retrieval Augmented Generation (with 5 group member agents and 1 manager agent)](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat_RAG.ipynb)
 
-[Automated Code Generation and Question Answering with Qdrant based Retrieval Augmented Agents](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_qdrant_RetrieveChat.ipynb)
+[Automated Code Generation and Question Answering with Qdrant based Retrieval Augmented Agents](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_RetrieveChat_qdrant.ipynb)
 
 ## Teachability
 
diff --git a/website/docs/topics/retrieval_augmentation.md b/website/docs/topics/retrieval_augmentation.md
index ca7222a404..366893cb98 100644
--- a/website/docs/topics/retrieval_augmentation.md
+++ b/website/docs/topics/retrieval_augmentation.md
@@ -119,8 +119,8 @@ ragproxyagent.initiate_chat(
 ## More Examples and Notebooks
 For more detailed examples and notebooks showcasing the usage of retrieval augmented agents in AutoGen, refer to the following:
 - Automated Code Generation and Question Answering with Retrieval Augmented Agents - [View Notebook](/docs/notebooks/agentchat_RetrieveChat)
-- Automated Code Generation and Question Answering with [PGVector](https://github.com/pgvector/pgvector) based Retrieval Augmented Agents - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_pgvector_RetrieveChat.ipynb)
-- Automated Code Generation and Question Answering with [Qdrant](https://qdrant.tech/) based Retrieval Augmented Agents - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_qdrant_RetrieveChat.ipynb)
+- Automated Code Generation and Question Answering with [PGVector](https://github.com/pgvector/pgvector) based Retrieval Augmented Agents - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_RetrieveChat_pgvector.ipynb)
+- Automated Code Generation and Question Answering with [Qdrant](https://qdrant.tech/) based Retrieval Augmented Agents - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_RetrieveChat_qdrant.ipynb)
 - Chat with OpenAI Assistant with Retrieval Augmentation - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_oai_assistant_retrieval.ipynb)
 - **RAG**: Group Chat with Retrieval Augmented Generation (with 5 group member agents and 1 manager agent) - [View Notebook](/docs/notebooks/agentchat_groupchat_RAG)